Determination of Optimal Distribution and Transportation Network (Wood Transportation in Iran)

Today, transportation network optimization has become one of the significant aspects of supply chain planning, and even a slight rise in productivity can significantly reduce costs of distribution of wood in the transportation network. In the forest based industry, given that transportation is the main cost of raw wood supply, using transportation planning, distribution should be done in a way so as to minimize the overall wood displacement. Such planning must meet the needs of all demand centers and the distribution supplier points must be used to their full capacity. Accordingly, the present study strived to find an optimal solution for transportation and distribution of raw wood from the main supplier points to small and large centers of wood and paper industries in Iran. This optimization simultaneously focuses on several products and is at the macroeconomic level of the country wood market. To achieve this goal, linear programming – Transportation Simplex Algorithm was used. The results show a significant fall in transportation costs and a more organized wood distribution network than the current situation. This cost reduction can be attributed to decisions about the optimal distribution of wood types, determining transport routes, and opting for the right type of truck supplier based on load tonnage and distance. This plummet in transportation costs plunges the cost of wood and wood products, which will surge competition in the business and will be of interest to manufacturers, distributors, customers and stakeholders in general

Estimation of conservation value of myrtle (Myrtus communis) using a contingent valuation method: a case study in a Dooreh forest area, Lorestan Province, Iran

Background: Around 2000 plant species occur naturally in Lorestan Province of which 250 species are medicinal and myrtle is one of them. Myrtle is a shrub whose leaves and fruits have medicinal value and thus, if managed and harvested properly, could produce sustained economic benefits. In recent years, however, over half of the myrtle site areas was destroyed, due to inappropriate management and excessive harvesting practices. Thus, coming up with a practical harvesting approach along with identifying those factors damaging the sites, seems to be very crucial. Methods: In our investigation, we calculated the conservation value per hectare of myrtle in the Dooreh forest area in Lorestan Province. Using the Contingent Valuation (CV) and Double Bounded Dichotomous Choice (DBDC) methods, we determined the willingness to pay (WTP) for myrtle conservation. The WTP was estimated with a logit model for which indices were obtained based on a maximum precision criterion. Results: The results showed that 86.67 per cent of people were willing to pay for the conservation of these myrtle sites. Average monthly WTP per family was calculated as $0.79. The annual conservation value in terms of WTP for the preservation of the myrtle sites in Dooreh was estimated as$102,525. Among the variables of the model presented, education had a positive impact, while the amount proposed for payment and family size had a negative impact on the WTP. Conclusions: Our estimate of the value of myrtle conservation should provide justification for policy makers and decision making bodies of natural resources to implement policies in order to conserve the natural sites of this species more effectively. Keywords: Conservation value, Myrtle, Contingent valuation method, Double Bounded Dichotomous method, Logit mode

The impact of political treaties and domestic and regional events on wood trade (A case study of Iran, Russia and the Warsaw Pact countries)

International trade has a significant share of the global economy; the issue of regulating foreign economic relations is a major contributor to political talks in the countries. Social, political and cultural events, including political and economic agreements, have a tremendous impact on the trade of countries. Forests around the world produce large amounts of wood and trade in products throughout the world. Since Iran has been trading in wood with Russia and other European countries about 100 years ago, this study was aimed at investigating the effects of political events on the bilateral exports of timber in Iran and Russia, as well as the effect of the Warsaw Treaty and its collapse on The wood trade of member countries with Iran was done using the gravity pattern. The results of the study show that the Warsaw Pact as a political treaty affects the trade relations of the countries of the covenant with Iran, and the per capita income of two trade partners, exchange rate changes and geographical distances on the volume of wood trade and Wood products have been effective. Results for both Iran and Russia show the impact of wood trade on political events as well as domestic laws and bills approved by the two countries

Analysis of Zagros forest structure using neighborhood-based indices (Case study: Ghalehgol forest, Khorramabad)

Analysis of forest structure is essential for enhanced understanding of forest ecology and management. In this study, the spatial structure of the existing species in Perk district of Ghaleh Gol site in Lorestan Province was explored. To this end, a 32-hectare region was 100% surveyed. To investigate the spatial structure, we used a set of indices including the Clark and Evans, uniform angles, Shannon-Wiener, mingling, Crown canopy and Crown canopy differentiation indices. The results showed the average values of 0.8 and 0.47 for Clark and Evans and uniform angles indices, respectively. This indicated random and cluster distribution patterns. In addition, Mean values of 0.25 and 0.06 were returned by Shannon - Weiner and mingling indices. Due to the dominant Oak coverage within the study area, Blend Low Index was additionally calculated. The mean Crown Canopy Index of 0.5 turned out a canopy dominance of Quercus  brantii, Acer cineracense, Crataegus sp. and Pyrus syriaca  over Lonicera nomularifolia and Amygdalus sp.. Moreover, the Crown Canopy Differentiation Index was calculated to quantify the differences between the levels of crown canopy in adjacent trees. This returned a mean value of 0.48 for the entire trees, which reflects the difference between the tree canopy levels. The results showed that the study site is currently undergoing an inappropriate biodiversity for woody species which yet shows better conditions compared to similarly-structured stands in the region. As a conclusion, proper forest planning measures should be carried out to prevent the recently experienced consistent loss of biodiversity

Evaluating of forest sustainability affected by tribal forestry (Case study: Shoul Abad-Lorestan, Iran)

The Zagros forests, covering approximately 5.2 million hectares of area, is considered as vital part of natural resources in Iran. Regulated and precise management plans to manage the Zagros forests is crucial regarding to its economic and social circumstances. Applying the local forestry, involving people’s participation in a near future, can be rewarding provided by making the possibility of its implementation. In this study, in order to find a transient policy to preparing the Shoul Abad area (Located in Lorestan Province) for the participatory forestry, tribal (local) forestry is considered as the best tool that can help to record, revise, and correct this kind of forestry. A number of three districts including Kamargap (where the tribal forestry is being implemented), Hiyye (where the tribal forestry is not being implemented) and Darre-Dang were selected and analyzed by FAO criteria (e.g. investigating the forest resources, biodiversity etc.). The forest sustainability was calculated by analyzing the data (regeneration, canopy coverage and biodiversity) gathered from the districts, using SAS software. Comparing the mean groups was carried out using SNK test. The results showed that the forest sustainability is higher in Kamargap district. Hence, applying the tribal forestry is now proposed to manage the forests located in Shoul Abad-Lorestan

Combining Counterfactuals With Shapley Values To Explain Image Models

With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task. Models trained on tabular data have witnessed significant progress in explanations of their underlying decision making processes by virtue of having a small number of discrete features. However, applying these methods to high-dimensional inputs such as images is not a trivial task. Images are composed of pixels at an atomic level and do not carry any interpretability by themselves. In this work, we seek to use annotated high-level interpretable features of images to provide explanations. We leverage the Shapley value framework from Game Theory, which has garnered wide acceptance in general XAI problems. By developing a pipeline to generate counterfactuals and subsequently using it to estimate Shapley values, we obtain contrastive and interpretable explanations with strong axiomatic guarantees

Impact of trade liberalization of wood industry on environmental quality in Iran in relation to wood business partners (Case study: Carbon dioxide emission)

Today, concern about environmental degradation and conservation is a priority. In this regard, the impact of trade on the environment is an important and growing issue in current politics. International trade affects the quality of the environment with respect to the three dimensions of scale, composition and technology. The carbon dioxide emission index is used to demonstrate environmental quality. The purpose of this study was to investigate the effects of the liberalization of the wood industry on Iran's environmental quality in relation to the 16 main wood trade partners within 1995-2015. In this study, the data panel method and the Stata software were used to estimate the model. The results of the model showed that the effect of the scale of 0.08 units and the effect of the combination of 0.427 units were overcome by the technical effect. Also, the results showed that due to the liberalization of the wood industry of Iran in relation to its wood partners, the combination of the three mentioned factors amounting to 0.424 units would reduce the quality of the environment

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R² = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R² = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R² = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only

Estimation of aboveground biomass using optical and radar images (Case study: Nav-e Asalem forests, Gilan)

Using remote sensing data is an applied method to estimate above ground biomass. In this study, satellite radar data of ALOS-2, with the full polarization and the optical data of Sentinel-2, has been used to estimate the aboveground biomass in the Nav-e Asalem forests, Gilan province. The backscattering coefficients at different polarization, the texture measures and target decomposition features of SAR images, obtained original and synthetic bands from optical images in three different combinations of radar images, optical images and the composition of radar and optical images, as inputs to the Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models were used. In order to measure aboveground biomass, 149 sample plots were laid out. Evaluation of ANN and MLR models using R2 and RMSE statistics showed that in all cases the ANN was better performance to estimate the aboveground biomass than MLR. The best results showed that the ANN from combined optical and radar data with R2 and RMSE, 0.86 and 31.62 Mg/ha (15.34%), respectively, can be the best applied method to estimate the aboveground biomass. The results of radar images and optical separately, with the R2 and RMSE for the modeling of aboveground biomass have been shown, respectively, 0.57 and 49.17 Mg/ha (23.85%) by radar images and 0.7 and 39.53 Mg/ha (19.17%) by the optical images, superior modeling to estimate aboveground biomass represents by optical imaging. The overall and more accurate results to estimate of aboveground biomass have been shown when we used combined radar and optical images with the ANN model